Comparative evaluation of supervised machine learning algorithms for breast cancer prediction using the Wisconsin diagnostic dataset

Breast cancer remains a leading cause of cancer-related mortality among women globally. Early diagnosis is critical to improving survival outcomes, and machine learning (ML) models have shown promise in enhancing predictive accuracy in medical diagnostics. This study presents a comprehensive comparative evaluation of six supervised ML algorithms: Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Decision Tree (DT), and Gaussian Naive Bayes (GNB), applied to the Breast Cancer Wisconsin (Diagnostic) dataset. Using RandomForestClassifier for feature importance ranking, SMOTE to address class imbalance, and StandardScaler for normalization, the models were trained and evaluated using an 80-20 train-test split. Performance was assessed based on accuracy, precision, recall, and F1-score. Logistic Regression achieved the highest overall performance (97.90% accuracy, 100% precision). Results indicate that linear models can outperform complex classifiers under well-prepared conditions. This study contributes to ML-aided diagnostics by identifying optimal algorithms for breast cancer prediction using clinical imaging-derived features.